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      TensorFlow1.5 新年全新教程(系列)
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        <p>本文介绍 TensorFlow1.5 新年全新教程(系列)<br><a id="more"></a></p>
<h1 id="TensorFlow1-5-新年全新教程-系列"><a href="#TensorFlow1-5-新年全新教程-系列" class="headerlink" title="TensorFlow1.5 新年全新教程(系列)"></a>TensorFlow1.5 新年全新教程(系列)</h1><blockquote>
<p>This article was original written by Jin Tian, welcome re-post, first come with <a href="https://jinfagang.github.io" target="_blank" rel="noopener">https://jinfagang.github.io</a> . but please keep this copyright info, thanks, any question could be asked via wechat: <code>jintianiloveu</code> </p>
</blockquote>
<p>很久没有更博客了，眨眼都已经2018年了，遥想去年跨年就好像发生在前天一样，预祝大家2019年猪年大吉。</p>
<p>闲话不多说。在家呆久了不学点东西感觉心虚，科技发展这么快，不脚踏实地开疆拓土怎么行呢？新年就要有新气象嘛，作为一位人工智能行业从业者，希望以一个过来的人的身份，带领更多的人在这条道路上披荆斩棘，开拓新的领域。工欲善其事必先利其器，TensorFlow1.5都已经发布了，我们还有什么理由不去学习一下最新的tf.data.Dataset API? 还有什么理由不期待一下TensorFlow Lite的终极版本以及专属于移动端的模型存储框架FlatBuf…感觉科技又前进了一个世纪，不过没有关系。凡事都得从当下做起。自从1.5版本发布 之后，tensorflow里面的很多API都将冻住了，并且会越来越规范化，为的正式迎接2018年深度学习应用落地的爆发之年。</p>
<p>闲话就说到这里了。我们首先从tensorflow的最新dataset API说起。</p>
<p>开始之前给大家安利一个工具：<a href="https://github.com/jinfagang/alfred" target="_blank" rel="noopener">alfred</a>, 专门为深度学习打造的工具，欢迎大家star， fork，enhance。我们接下来用它来随时爬几张猪啊狗啊的图片。</p>
<h2 id="tf-data-Dataset"><a href="#tf-data-Dataset" class="headerlink" title="tf.data.Dataset"></a>tf.data.Dataset</h2><p>这个以前是在contrib下面的一个接口，现在放到了data下面，可以说是非常正统的tensorflow数据导入接口了。以前都是用tfrecords，现在不管是从单张图片，从文件夹路径，还是从numpy array类型的数据，都非常方便了。</p>
<p>假设我们有一个图片分类的简单任务。我们的目录是这样的：</p>
<figure class="highlight 1c"><table><tr><td class="code"><pre><div class="line">-data</div><div class="line">    <span class="string">|-dog</span></div><div class="line">    <span class="string">|-pig</span></div><div class="line">    <span class="string">|-...</span></div></pre></td></tr></table></figure>
<p>这个猪啊狗啊的图片alfred可以帮你爬取：</p>
<figure class="highlight stylus"><table><tr><td class="code"><pre><div class="line">sudo pip3 install alfred-py</div><div class="line">alfred scrap image -<span class="selector-tag">q</span> <span class="string">'dog'</span></div><div class="line">alfred scrap image -<span class="selector-tag">q</span> <span class="string">'pig'</span></div></pre></td></tr></table></figure>
<p>每个类别装了许多同一类的图片。那直接读取到python的list，然后转成tensor，通过<code>tf.data.Dataset</code>就可以读入到tensorflow里面。</p>
<figure class="highlight python"><table><tr><td class="code"><pre><div class="line"><span class="keyword">import</span> tensorflow <span class="keyword">as</span> tf</div><div class="line"><span class="keyword">import</span> os</div><div class="line"></div><div class="line"></div><div class="line">NUMC_CLASSES = <span class="number">2</span></div><div class="line"></div><div class="line"></div><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">load_image</span><span class="params">()</span>:</span></div><div class="line">    train_dir = <span class="string">'data'</span></div><div class="line">    all_classes = []</div><div class="line">    all_images = []</div><div class="line">    all_labels = []</div><div class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> os.listdir(train_dir):</div><div class="line">        current_dir = os.path.join(train_dir, i)</div><div class="line">        <span class="keyword">if</span> os.path.isdir(current_dir):</div><div class="line">            all_classes.append(i)</div><div class="line">            <span class="keyword">for</span> img <span class="keyword">in</span> os.listdir(current_dir):</div><div class="line">                <span class="keyword">if</span> img.endswith(<span class="string">'png'</span>) <span class="keyword">or</span> img.endswith(<span class="string">'jpg'</span>):</div><div class="line">                    all_images.append(os.path.join(current_dir, img))</div><div class="line">                    all_labels.append(all_classes.index(i))</div><div class="line">    <span class="keyword">return</span> all_images, all_labels, all_classes</div><div class="line"></div><div class="line"></div><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">train</span><span class="params">()</span>:</span></div><div class="line">    all_images, all_labels, all_classes = load_image()</div><div class="line">    print(all_classes)</div><div class="line">    <span class="comment"># convert all images list to tensor, using Dataset API to load</span></div><div class="line">    train_data = tf.data.Dataset.from_tensor_slices((tf.constant(all_images), tf.constant(all_labels)))</div><div class="line">    iterator = tf.data.Iterator.from_structure(train_data.output_types, train_data.output_shapes)</div><div class="line"></div><div class="line">    next_elem = iterator.get_next()</div><div class="line">    train_init_op = iterator.make_initializer(train_data)</div><div class="line"></div><div class="line">    <span class="keyword">with</span> tf.Session() <span class="keyword">as</span> sess:</div><div class="line">        sess.run(train_init_op)</div><div class="line">        <span class="keyword">while</span> <span class="keyword">True</span>:</div><div class="line">            <span class="keyword">try</span>:</div><div class="line">                print(sess.run(next_elem))</div><div class="line">            <span class="keyword">except</span> tf.errors.OutOfRangeError:</div><div class="line">                print(<span class="string">'data iterator finish.'</span>)</div><div class="line">                <span class="keyword">break</span></div><div class="line"></div><div class="line"><span class="keyword">if</span> __name__ == <span class="string">'__main__'</span>:</div><div class="line">    train()</div></pre></td></tr></table></figure>
<p>我们可以看到输出结果是：</p>
<figure class="highlight scheme"><table><tr><td class="code"><pre><div class="line">[<span class="symbol">'dog</span>', <span class="symbol">'pig</span>']</div><div class="line">(<span class="name">b</span><span class="symbol">'dog_00.jpg</span>', <span class="number">0</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'dog_01.jpg</span>', <span class="number">0</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_00.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_01.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_010.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_02.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_03.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_04.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_05.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_06.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_07.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_08.jpg</span>', <span class="number">1</span>)</div><div class="line">(<span class="name">b</span><span class="symbol">'pig_09.jpg</span>', <span class="number">1</span>)</div><div class="line">data iterator finish.</div></pre></td></tr></table></figure>
<p>图片和标签都已经获得。用最新的Dataset API中的 <code>from_tensor_slices</code>可以非常方便的从list中将数据导入。</p>
<p>很多时候我们都需要对图片进行预处理，比如我们需要做一个检测数据集，我们要读入label和bbox，这个时候label需要one-hot，我们就需要对这个东西进行预处理，这个时候map就有用了。</p>
<h2 id="tf-data-Dataset-map"><a href="#tf-data-Dataset-map" class="headerlink" title="tf.data.Dataset.map"></a>tf.data.Dataset.map</h2><p>这还没有完，我们的目的是操作每一张图片，做一些变换。或者对label进行一些处理，比如one-hot。在最新的dataset API中也有map函数进行操作。可以在这个map方法里，指定所有应有的操作。</p>
<figure class="highlight python"><table><tr><td class="code"><pre><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">input_map_fn</span><span class="params">(img_path, label)</span>:</span></div><div class="line">    <span class="comment"># do some process to label</span></div><div class="line">    one_hot = tf.one_hot(label, NUMC_CLASSES)</div><div class="line">    img_f = tf.read_file(img_path)</div><div class="line">    img_decodes = tf.image.decode_image(img_f, channels=<span class="number">3</span>)</div><div class="line">    <span class="keyword">return</span> img_decodes, one_hot</div></pre></td></tr></table></figure>
<p>然后将train_data加上即可。</p>
<figure class="highlight ini"><table><tr><td class="code"><pre><div class="line"><span class="attr">train_data</span> = train_data.map(input_map_fn)</div></pre></td></tr></table></figure>
<p>最终我们可以看到熟悉的，图片值 + one_hot label的训练数据。如果是对于像多标签分类，目标检测这样的任务label，也是做同样的处理。只要能保证前期的输入能在后期的网络中拿到就行了。</p>
<p>好了，现在tensorflow全新的数据导入API应该已经融会贯通了。下一篇大家等待更新，博主这还得去乡下拜个年。</p>

      
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